code: def optimize(self):
      for i in range(iterations):
          print("iteration ", i)
          # Get the global best particle
          gBest = self.swarm[0]
          for j in range(swarmSize):
              pBest = self.swarm[j].pBest
              if self.f(pBest) > self.f(gBest):
                  gBest = pBest
          solution = gBest
          # Update position of each paricle
          for k in range(swarmSize):
              self.swarm[k].updateVelocities(gBest)
              self.swarm[k].updatePositions()
              self.swarm[k].satisfyConstraints()
          # Update the personal best positions
          for l in range(swarmSize):
              pBest = self.swarm[l].pBest
              if self.f(self.swarm[l]) > self.f(pBest):
                  self.swarm[l].pBest = self.swarm[l].pos
      return solution
  
file_path: advisor/advisor_server/suggestion/algorithm/particle_swarm_optimization.py
imports: from __future__ import print_function
  import json
  import random
  from suggestion.models import Study
  from suggestion.models import Trial
  from suggestion.algorithm.abstract_algorithm import AbstractSuggestionAlgorithm
label_desc: Use image node_id 2 for calling the ParticleSwarmOptimizer obj's underlying member method code with example usage: obj.optimize() and returns: solution